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* Refactor full-text query documentation Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add examples and parameter descriptions Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add multi-match query Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add query string field format Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Query string examples Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add regular expressions and fuzziness Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add wildcard and regex warning Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added more query string format Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added multi-field sections Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Rewrite minimum should match section Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added allow expensive queries section Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add simple query string query Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Small rewrites Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add intervals query Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Include discover in query string syntax Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Link and index page fix Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Implemented editorial comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> --------- Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Co-authored-by: Melissa Vagi <vagimeli@amazon.com>
32 lines
2.2 KiB
Markdown
32 lines
2.2 KiB
Markdown
---
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layout: default
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title: Term-level queries
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has_children: true
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has_toc: false
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nav_order: 20
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---
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# Term-level queries
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Term-level queries search an index for documents that contain an exact search term. Documents returned by a term-level query are not sorted by their relevance scores.
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When working with text data, use term-level queries for fields mapped as `keyword` only.
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Term-level queries are not suited for searching analyzed text fields. To return analyzed fields, use a [full-text query]({{site.url}}{{site.baseurl}}/opensearch/query-dsl/full-text/).
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## Term-level query types
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The following table lists all term-level query types.
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Query type | Description
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:--- | :---
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[`term`]({{site.url}}{{site.baseurl}}/query-dsl/term/term/) | Searches for documents containing an exact term in a specific field.
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[`terms`]({{site.url}}{{site.baseurl}}/query-dsl/term/terms/) | Searches for documents containing one or more terms in a specific field.
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[`terms_set`]({{site.url}}{{site.baseurl}}/query-dsl/term/terms-set/) | Searches for documents that match a minimum number of terms in a specific field.
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[`ids`]({{site.url}}{{site.baseurl}}/query-dsl/term/ids/) | Searches for documents by document ID.
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[`range`]({{site.url}}{{site.baseurl}}/query-dsl/term/range/) | Searches for documents with field values in a specific range.
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[`prefix`]({{site.url}}{{site.baseurl}}/query-dsl/term/prefix/) | Searches for documents containing terms that begin with a specific prefix.
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[`exists`]({{site.url}}{{site.baseurl}}/query-dsl/term/exists/) | Searches for documents with any indexed value in a specific field.
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[`fuzzy`]({{site.url}}{{site.baseurl}}/query-dsl/term/fuzzy/) | Searches for documents containing terms that are similar to the search term within the maximum allowed [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance). The Levenshtein distance measures the number of one-character changes needed to change one term to another term.
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[`wildcard`]({{site.url}}{{site.baseurl}}/query-dsl/term/wildcard/) | Searches for documents containing terms that match a wildcard pattern.
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[`regexp`]({{site.url}}{{site.baseurl}}/query-dsl/term/regexp/) | Searches for documents containing terms that match a regular expression. |